Ocean Heat
Identification
1. Indicator Description
This indicator describes trends in the amount of heat stored in the world's oceans between 1955 and
2020. The amount of heat in the ocean, or ocean heat content, is an important indicator of climate
change because the oceans ultimately absorb a large portion of the extra energy that greenhouse gases
trap near the Earth's surface. Ocean heat content also plays an important role in the Earth's climate
system because heat from ocean surface waters provides energy for storms and thereby influences
weather patterns.
2. Revision History
April 2010: Indicator published.
December 2012: Updated indicator with data through 2011.
August 2013: Updated indicator with data through 2012.
May 2014: Updated indicator with data through 2013.
June 2015: Updated indicator with data through 2014.
August 2016: Updated indicator with data through 2015.
April 2021: Updated indicator with data through 2020, added new IAP data source, updated
MRI/JMA analysis version, and added Figure 2 to present measurements from a
deeper portion of the water column.
Data Sources
3. Data Sources
This indicator is based on analyses conducted by four government organizations:
• Australia's Commonwealth Scientific and Industrial Research Organisation (CSIRO)
• The Japan Meteorological Agency's Meteorological Research Institute (MRI/JMA)
• The U.S. National Oceanic and Atmospheric Administration (NOAA)
• The Chinese Academy of Sciences' Institute of Atmospheric Physics (IAP)
NOAA, IAP, and MRI/JMA used data from the World Ocean Database (WOD) for their analyses. MRI/JMA
also used data from two other data sets: the Global Temperature-Salinity Profile Program (GTSPP, which
was used to fill gaps in the WOD since 1990) and the Argo Project. CSIRO used two data sets: ocean
temperature profiles in the UK Met Office's ENACT/ENSEMBLES version 4 (EN4) database and data
collected by thousands of Argo profiling floats. Information on Argo project can be found at:
https://argo.ucsd.edu. Additionally, CSIRO included bias-corrected Argo data, as described in Barker et
al. (2011), and bias-corrected expendable bathythermograph (XBT) data from Wijffels et al. (2008).
NOAA also used data from the WOA in addition to the WOD.
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4. Data Availability
EPA developed Figure 1 using trend data from several ongoing studies. Data and documentation from
these studies can be found at the following links:
• CSIRO: www.cmar.csiro.au/sealevel/thermal expansion ocean heat timeseries.html. Select
"GOHC_recons_version3.1_1950_2012_CLIM_sbcal2tmosme_OBS_bcax_0700m.dat" to
download data through 2012. See additional documentation in Domingues et al. (2008).
Updated data through 2015 were provided by the author, Catia Domingues.
• IAP: http://159.226.119.60/cheng. Updated data through 2020 were provided by the author,
Lijing Cheng, before being posted on this website. See additional documentation in Cheng et al.
(2017).
• MRI/JMA: www.data.ima.go.ip/gmd/kaivou/english/ohc/ohc global en.html. Select "Heat
content anomaly time series."
• NOAA, 0-700 meters: www.nodc.noaa.gov/OC5/3M HEAT CONTENT. Under "Heat Content,"
select "Basin time series fields." Then, under "Yearly from 1955 to 2020," select the "0 - 700" file
under "World." See additional documentation in Levitus et al. (2009).
• NOAA, 0-2,000 meters: www.nodc.noaa.gov/OC5/3M HEAT CONTENT. Under "Heat Content,"
select "Basin time series fields." Then, under "Pentadal from 1955 to 2020," select the "0 -
2000" file under "World." See additional documentation in Levitus et al. (2012).
The underlying data for this indicator come from a variety of sources. Some of these data sets are
publicly available; others consist of samples gathered by the authors of the source papers, and these
data might be more difficult to obtain online. WOA and WOD data and descriptions of data are available
on NOAA's National Centers for Environmental Information (NCEI) website at:
www.nodc.noaa.gov/OC5/3M HEAT CONTENT. The EN4 database is available at:
www.metoffice.gov.uk/hadobs/en4.
Methodology
5. Data Collection
Figure 1 of this indicator reports on the amount of heat stored in the ocean from sea level to a depth of
700 meters, which accounts for approximately 17.5 percent of the total global ocean volume
(calculation from Catia Domingues, CSIRO). Figure 2 reports on the amount of heat stored to a depth of
2,000 meters, which accounts for approximately 48.5 percent of the total global ocean volume
(calculation from Catia Domingues, CSIRO). Each of the studies used to develop this indicator uses
several ocean temperature profile data sets to calculate an ocean heat content trend line.
Several different devices are used to sample temperature profiles in the ocean. Primary methods used
to collect data for this indicator include XBT; mechanical bathythermographs (MBT); Argo profiling
floats; reversing thermometers; and conductivity, temperature, and depth sensors (CTD). These
instruments produce temperature profile measurements of the ocean water column by recording data
on temperature and depth. The exact methods used to record temperature and depth vary. For
instance, XBTs use a fall rate equation to determine depth, whereas other devices measure depth
directly.
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Each of the studies used to develop this indicator relies on different combinations of devices; for
example, the CSIRO analysis excludes MBT data. More information on the main studies and their
respective methods can be found at:
• CSIRO: Domingues et al. (2008) and:
www.cmar.csiro.au/sealevel/thermal expansion ocean heat timeseries.html.
• IAP: Cheng et al. (2017) and: http://159.226.119.60/cheng.
• MRI/JMA: Ishii etal. (2017).
• NOAA, 0-700 meters: Levitus et al. (2009) and: www.nodc.noaa.gov/OC5/3M HEAT CONTENT.
• NOAA, 0-2,000 meters: Levitus et al. (2012) and:
www.nodc.noaa.gov/OC5/3M HEAT CONTENT.
Studies that measure ocean temperature profiles are generally designed using in situ oceanographic
observations and analyzed over a defined and spatially uniform grid (Ishii and Kimoto, 2009). For
instance, the WOA data set consists of in situ measurements of climatological fields, including
temperature, measured in a 1-degree grid. Sampling procedures for WOD and WOA data are provided
by NOAA's NCEI at: www.nodc.noaa.gov/OC5/indprod.html. More information on the WOA sample
design in particular can be found at: www.nodc.noaa.gov/OC5/WOAQ5/pr woa05.html.
At the time of this indicator's last update, data from CSIRO were available through 2015, data from IAP
were available through 2020, MRI/JMA data were available through 2020, and NOAA data were
available through 2020. NOAA's 0-2,000 meter data are plotted from 1957 through 2018 because each
annual value is derived from a pentadal average, the earliest of which covered 1955-1959 and the most
recent of which covered 2016-2020.
6. Indicator Derivation
While details of data analysis are particular to the individual study, in general, temperature profile data
were averaged monthly at specific depths within rectangular grid cells. In some cases, interpolation
techniques were used to fill gaps where observational spatial coverage was sparse. Additional steps
were taken to correct for known biases in XBT data. Finally, temperature observations were used to
calculate ocean heat content through various conversions.
Barker et al. (2011) describe instrument biases and procedures for correcting for these biases. For more
information about interpolation and other analytical steps, see Cheng et al. (2017), Ishii et al. (2017),
Domingues et al. (2008), Levitus et al. (2009), Levitus et al. (2012), and references therein.
Each study used a different long-term average as a baseline. To allow more consistent comparison, EPA
adjusted each curve such that its 1971-2000 average would be set at zero. Choosing a different baseline
period would not change the shape of the data over time. Although some of the studies had pre-1955
data, Figure 1 begins at 1955 for consistency. The current CSIRO data series is based on updates to the
original data set provided in Domingues et al. (2008) and plotted with a start date of 1960. The updated
data set excludes 1955-1959: the authors (Domingues et al.) have expressed diminished confidence in
their data set for this period because there are fewer ocean observations in the early part of the record.
The data set also uses a three-year running mean to smooth the data.
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NOAA calculated its 0-2,000 meter data set using pentadal (five-year) averages due to the relative
paucity of underlying data points in the deep ocean, particularly for the early portion of the time series.
NOAA published annual values that are derived from this pentadal data product.
Indicator Development
EPA has periodically enhanced this indicator to reflect ongoing improvements in data sources and
methods of estimating global ocean heat content. Prior to 2018, this indicator focused on data from
three sources—CSIRO, MRI/JMA, and NOAA—covering the top 700 meters of the world oceans. With
the publication of enhanced methods for reconstructing deeper ocean heat trends in Cheng et al.
(2017), EPA was able to add a new source (IAP) to Figure 1 and was also able to create Figure 2 with data
for the top 2,000 meters of the ocean. Figure 2 also includes improved estimates for the top 2,000
meters from NOAA and MRI/JMA.
For reference, Figure TD-1 shows the relative changes in heat content of the 0-700 meter and 700-
2,000 meter layers of the world's oceans, based on the IAP analysis (updated from Cheng et al., 2017).
The total height of the stack represents the total change across both of these segments of the water
column. Like Figures 1 and 2, Figure TD-1 is normalized to the 1971-2000 mean.
Figure TD-1. Relative Contributions to Changes in Ocean Heat Content in the IAP Data Set, by Depth,
1955-2020
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www.nodc.noaa.gov/OC5/WOD/docwod.html. Each of the data collection techniques involves different
QA/QC measures. For example, a summary of studies concerning QA/QC of XBT data is available from
NCEI at: www.nodc.noaa.gov/OC5/XBT BIAS/xbt bibliographv.html. The same site also provides
additional information about QA/QC of ocean heat data made available by NCEI.
All of the analyses performed for this indicator included additional QA/QC steps. In each of the main
studies used in this indicator, the authors carefully describe QA/QC methods, or provide the relevant
references.
Analysis
8. Comparability Over Time and Space
Analysis of raw data is complicated because data come from a variety of observational methods, and
each observational method requires certain corrections to be made. For example, systematic biases in
XBT depth measurements have recently been identified. These biases were shown to lead to erroneous
estimates of ocean heat content through time. Each of the main studies used in this indicator corrects
for these XBT biases. Correction methods are slightly different among studies and are described in detail
in each respective paper. More information on newly identified biases associated with XBT can be found
in Barker et al. (2011).
This indicator presents multiple independently derived trend lines to compare different estimates of
ocean heat content overtime. Each estimate is based on analytical methods that have been applied
consistently over time and space. General agreement among trend lines, despite some year-to-year
variability, indicates a robust trend.
9. Data Limitations
Factors that may impact the confidence, application, or conclusions drawn from this indicator are as
follows:
1. Data must be carefully reconstructed and filtered for biases because of different data collection
techniques and uneven sampling over time and space. Various methods of correcting the data
have led to slightly different versions of the ocean heat trend line.
2. In addition to differences among methods, some biases may be inherent in certain methods.
The older MBT and XBT technologies have the highest uncertainty associated with
measurements.
3. Limitations of data collection over time and especially over space affect the accuracy of
observations. In some cases, interpolation procedures were used to complete data sets that
were spatially sparse.
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10. Sources of Uncertainty
Uncertainty measurements can be made by the organizations responsible for data collection, and they
can also be made during subsequent analysis. One example of uncertainty measurements performed by
an agency is available for the WOA at: www.nodc.noaa.gov/OC5/indprod.html.
Error estimates associated with each of the curves in Figure 1 are discussed in Cheng et al. (2017),
Domingues et al. (2008), Ishii et al. (2017), Hirahara et al. (2014), and Levitus et al. (2009). Error
estimates for Figure 2 are discussed in Cheng et al. (2017), Levitus et al. (2012), Ishii et al. (2017), and
Hirahara et al. (2014).
Each of the data files listed in Section 4 includes some type of error or uncertainty around each data
point. Figures TD-2, TD-3, TD-4, and TD-5 show these error or uncertainty values. To be true to each of
the original sources, these figures show the values as they were provided, and they use the
corresponding nomenclature as reported by the organization that published the data. The shaded error
or uncertainty bands in these four figures represent different statistical properties, so they are not
directly comparable to each other. Figure TD-3 shows monthly values; it shows the 700-2,000 meter
segment of the water column (not 0-2,000 meters) because that is how IAP reported the original data.
No attempt was made to transform these numbers into annual error estimates or to calculate combined
error values for the sum of the 0-700 meter and 700-2,000 meter sections.
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Figure TD-2. Ocean Heat Content in the Top 700 Meters from the CSIRO Data Set, 1960-2015, with
One Standard Deviation
Data source: CSIRO 2016 update to data originally published in Domingues et al. (2008).
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Figure TD-3. Ocean Heat Content for 0-700 Meters and 700-2,000 Meters from the Monthly IAP Data
Sets, 1955-2017, with 95 Percent Confidence Intervals
20 T 1 , 7
-15 4 T T t T T T T—
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
Data source: Cheng et al. (2017).
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Figure TD-4. Ocean Heat Content for the Top 2,000 Meters from the MRI/JMA Data Set,
1955-2020, with 95 Percent Confidence Intervals
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1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Year
Data source: MRI/JMA data portal: www,data.ima.go.io/amd/kaivou/enaHsh/ohc/ohc global en.html.
1971-^000 average
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NOAA 0-700m
NOAA 0-2000m
Figure TD-5. Ocean Heat Content in the Top 700 Meters and the Top 2,000 Meters in the NOAA Data
Sets, 1955-2020, with Standard Errors
25
-15
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Year
Data source: NOAA data portal: www.nodc.noaa.gov/OC5/3M HEAT CONTENT.
11. Sources of Variability
Weather patterns, seasonal changes, multiyear climate oscillations, and many other factors could lead
to day-to-day and year-to-year variability in ocean temperature measurements at a given location. This
indicator addresses some of these forms of variability by aggregating data over time and space to
calculate annual values for global ocean heat content. The overall increase in ocean heat over time (as
shown by all of the analyses) far exceeds the range of interannual variability in ocean heat estimates.
12. Statistical/Trend Analysis
Cheng et al. (2017), Domingues et al. (2008), Levitus et al. (2009), and Levitus et al. (2012) have all
calculated linear trends and corresponding error values for their respective ocean heat time series. Ishii
et al. (2017) calculated trends using the leading empirical orthogonal function interpolated to a 1-degree
grid; see Hirahara et al. (2014) for more details. Exact time frames and slopes vary among the
publications, but they all reveal a statistically significant upward trend (i.e., increasing ocean heat over
time).
References
Barker, P.M., J.R. Dunn, C.M. Domingues, and S.E. Wijffels. 2011. Pressure sensor drifts in Argo and their
impacts. J. Atmos. Oceanic Tech. 28:1036-1049.
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Cheng, L., K.E. Trenberth, J. Fasullo, T. Boyer, J. Abraham, and J. Zhu. 2017. Improved estimates of ocean
heat content from 1960 to 2015. Science Advances 3(3):el601545.
Domingues, C.M., J.A. Church, N.J. White, P.J. Gleckler, S.E. Wijffels, P.M. Barker, and J.R. Dunn. 2008.
Improved estimates of upper-ocean warming and multi-decadal sea-level rise. Nature 453:1090-1093.
Hirahara, S., M. Ishii, and Y. Fukuda. 2014. Centennial-scale sea surface temperature analysis and its
uncertainty. J. Climate 27(l):57-75.
Ishii, M., and M. Kimoto. 2009. Reevaluation of historical ocean heat content variations with time-
varying XBT and MBT depth bias corrections. J. Oceanogr. 65:287-299.
Ishii, M., Y. Fukuda, H. Hirahara, S. Yasui, T. Suzuki, and K. Sato. 2017. Accuracy of global upper ocean
heat content estimation expected from present observational data sets. SOLA 13:163-167.
https://www.istage.ist.go.ip/article/sola/13/0/13 2017-030/ article. doi:10.2151/sola.2017-030
Levitus, S., J.I. Antonov, T.P. Boyer, R.A. Locarnini, H.E. Garcia, and A.V. Mishonov. 2009. Global ocean
heat content 1955-2008 in light of recently revealed instrumentation problems. Geophys. Res. Lett.
36:L07608.
Levitus, S., J.I. Antonov, T.P. Boyer, O.K. Baranova, H.E. Garcia, R.A. Locarnini, A.V. Mishonov, J.R.
Reagan, D. Seidov, E.S. Yarosh, and M.M. Zweng. 2012. World ocean heat content and thermosteric sea
level change (0-2000 m), 1955-2010. Geophys. Res. Lett. 39(10):L10603. doi:10.1029/2012GL051106
Wijffels, S.E., J. Willis, C.M. Domingues, P. Barker, N.J. White, A. Gronell, K. Ridgway, and J.A. Church.
2008. Changing expendable bathythermograph fall rates and their impact on estimates of thermosteric
sea level rise. J. Climate 21:5657-5672.
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